CN115328463B - Visual business arrangement design system - Google Patents
Visual business arrangement design system Download PDFInfo
- Publication number
- CN115328463B CN115328463B CN202210914882.7A CN202210914882A CN115328463B CN 115328463 B CN115328463 B CN 115328463B CN 202210914882 A CN202210914882 A CN 202210914882A CN 115328463 B CN115328463 B CN 115328463B
- Authority
- CN
- China
- Prior art keywords
- image
- component
- module
- micro
- jar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000000007 visual effect Effects 0.000 title claims abstract description 30
- 238000013461 design Methods 0.000 title claims abstract description 16
- 238000011068 loading method Methods 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000003708 edge detection Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 238000004806 packaging method and process Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 7
- 238000010276 construction Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 9
- 230000008878 coupling Effects 0.000 description 7
- 238000010168 coupling process Methods 0.000 description 7
- 238000005859 coupling reaction Methods 0.000 description 7
- 230000007246 mechanism Effects 0.000 description 7
- 230000000712 assembly Effects 0.000 description 6
- 238000000429 assembly Methods 0.000 description 6
- 238000000034 method Methods 0.000 description 6
- 239000002244 precipitate Substances 0.000 description 4
- 230000010354 integration Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 238000012550 audit Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005538 encapsulation Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000000889 atomisation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/34—Graphical or visual programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/60—Editing figures and text; Combining figures or text
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Probability & Statistics with Applications (AREA)
- Character Input (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a visual business arrangement-based design system, which comprises a front-end canvas, front-end and back-end resolvers, an application starting module, a component writing module, a component using module and a self-defined component loading module. The invention expresses the arranging logic of the service system in a visual mode, so that the background key logic expression of the service system is not a black box any more; many new industrial components can be developed based on atomic components, including implementing business collaboration and data linkage with other industrial application systems; through the visual proposal, more people can participate in the background logic construction of the business system, which is a supplementary enhancement to the traditional development mode.
Description
Technical Field
The invention relates to the technical field of visual arrangement, in particular to an arrangement design system based on visual services.
Background
The visual editing technology is a comparatively general editing technology, is most applied at the data processing level at present, is very mature in application of the visual editing technology based on data flow, and mainly realizes a technical path mainly comprising two kinds of visual data editing tools of light application type represented by node-red and n8n, and also comprises an integrated data of business big data products of an Ali PAI and Microsoft cloud computing manufacturer and an artificial intelligent component editing tool, and a distributed scheduling data editing tool is calculated by means of containerization and cloud.
The atomization component is a technology and a service separation, the service is expressed through visual arrangement, the service and the technology are decoupled, the operation is configured at a time based on the cloud primary technology, the whole operation mechanism is very light, high cohesion is adopted in the process, and low coupling is adopted in the process. Most of the business orchestration currently on the market is based on the business integration mode of micro-services, which is relatively heavy in business coupling, some orchestration tools then mainly surround the data processing layer, with very few visual orchestration systems for such highly atomized components of the business system.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a visual business arrangement design system to overcome the technical problems existing in the prior related art.
For this purpose, the invention adopts the following specific technical scheme:
a layout design system based on visual service comprises a front canvas, front and back resolvers, an application starting module, a component writing module, a component using module and a self-defining component loading module; the front-end canvas is used for providing canvas at the front end of the system; the front-end and back-end resolvers are used for resolving data at the front end and the back end of the system; the application starting module is used for finishing the starting of the application module; the module writing module is used for customizing writing of templates at the back end and the front end of the module; the component using module is used for creating a micro-stream by a user and testing the micro-stream, and after the test is correct, the user issues the micro-stream; the custom component loading module is used for starting and running the micro-flow module and finishing the registration of component classes in the jar package.
Further, when the modules are written in the back end and the front end of the custom component, if the modules are written in the back end of the custom component, the back end of the custom component is downloaded, IDE is used for automatically writing Java codes, the Java codes are packaged into jar packages and uploaded, the jar packages are subjected to background auditing, after the auditing is passed, a user issues the modules, and the micro-flow module dynamically pulls the jar packages and registers component classes with specified class names.
Further, when the front end template of the custom component is written, the front end template of the custom component is downloaded, the front end component code is written, the code is put on shelf and checked in the background, the front end part of the component is released, and the released custom component is automatically loaded when the micro-flow canvas is opened by the front end.
Further, when the micro-flow module in the custom component loading module is started and operated to finish the registration of the component class in the jar packet, if the micro-flow module is started, the jar packet information of the published component is pulled from the database, and the jar packet is pulled from the minio to finish the registration of the component class in the jar packet.
Further, in the custom component loading module, if the micro-flow module operates and the user clicks the component to issue, broadcasting each micro-flow instance, and pulling the jar packet from the minio to complete the registration of the component class in the jar packet;
if the micro-flow module operates and is a timing task, periodically pulling the published jar packet information, and pulling the jar packet from the minio to finish the registration of the component class in the jar packet.
Further, during background auditing, auditing is carried out on characters and pictures in the jar packet, and auditing is carried out on the pictures, wherein the auditing comprises image preprocessing, image feature extraction and image sensitive information retrieval.
Further, in the image preprocessing, denoising is performed on the image, let r (x, y) be the image to be processed, q (x, y) be the image subjected to the mean filtering, and then the image is expressed as:
wherein omega is a coordinate set of all pixel points in a window neighborhood calculated by the mean filter, K is the total number of all pixel points in the window neighborhood, and x and y are respectively the abscissa and the ordinate in the picture;
the center point of the image is set to (x 0 ,y 0 ) Based on the descending order of the gray values of the pixels, after all the pixel points in the neighborhood taking the center point as the center are ordered, taking the intermediate value as the current pixel point;
the original gray value of the center pixel is replaced with a median value using a 3 x 3 template, and the median value is filtered, obtaining an image f (x, y) subjected to twice filtering, and denoising the image at the same time;
for a denoised image f (x, y), the gradient at a certain pixel (x, y) in the image is represented as a vector G:
where f is the de-noised image,is a vector parameter;
calculating the gradient of f (x, y) by using a look-up processing method, and carrying out gradient correction by using the following formula:
wherein T is a non-negative gradient threshold and G [ f (x, y) ] is a gradient vector of the denoised image f (x, y).
Further, during the image feature extraction, the color brightness of the image is represented by a color histogram, and the calculation formula of the color histogram is as follows:
wherein L is a gray value, n l The number of the first gray level pixels is the number of the first gray level pixels, and N is the sum of the image pixels;
and (3) performing image edge detection by using a Sobel operator, wherein the calculation formula of each gray value of an edge detection matrix is as follows:
wherein G is x G (G) y The calculation formulas are respectively used for convolution in the horizontal direction and the vertical direction;
calculating image texture energy:
wherein p (i, j) is the gray level probability of the image from the horizontal coordinate i to the horizontal coordinate j in a unit distance, and n is a non-zero natural number;
the thickness of the texture is expressed by the moment of inertia, which is expressed as follows:
wherein F is 2 The smaller the image texture is, the shallower the image texture is, n is a non-zero natural number, the complexity of the image texture is represented by an entropy value, and a calculation formula is as follows:
wherein F is 3 The larger the image is, the more complex the image is, n is a non-zero natural number, and the similarity of the row direction and the column direction of textures in the image is obtained according to a calculation formula:
wherein mu is x 、μ y Is the average value of row direction and column direction, sigma x 、σ y The variance is in the row direction and the column direction, and n is a non-zero natural number;
by calculating F 1 、F 2 、F 3 F (F) 4 And (3) obtaining the texture characteristics of the image and extracting the sensitive information in the image.
Further, during the image sensitive information retrieval, the image is divided into 3×3 blocks, the image is converted into HSV space from RGB space, and the characteristics of the convolutional neural network are extracted and normalized;
and (5) weighting the image features and fusing the feature vectors, inputting the image features, and completing retrieval of the sensitive information.
Further, if the image contains text information, filtering is performed according to the extracted image characteristics, and text characters are detected.
The beneficial effects of the invention are as follows:
(1) The invention adopts a light application scheduling mode, introduces the idea of fass, ensures that the whole system has high internal cohesion and low external coupling, then adopts a cloud native technology to develop the capability based on the fass mode, and ensures that a third party can develop and precipitate new industrial service and data components based on the current component capability in a very light weight way. The whole operation mechanism is very light, high cohesion is adopted in the whole operation mechanism, low coupling is adopted in the whole operation mechanism, more than 20 atomic assemblies are provided for products, more data processing assemblies can be developed based on the atomic assemblies and the opening capability, and the integration and communication of a business system based on the mode with external services and data are realized, so that the business system is supported to realize the cooperation of the business and the data, and the efficiency of developing a core business system is improved through a large number of visual arrangement service precipitates. And the system can automatically audit the characters and pictures of the jar packet, so that the efficiency of developing a core service system is further improved.
(2) The invention expresses the arranging logic of the service system in a visual mode, so that the background key logic expression of the service system is not a black box any more; many new industrial components can be developed based on atomic components, including implementing business collaboration and data linkage with other industrial application systems; through the visual proposal, more people can participate in the background logic construction of the business system, which is a supplementary enhancement to the traditional development mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a logic diagram of application launch and request handling in a visual business based orchestration design system according to embodiments of the present invention;
FIG. 2 is a flow chart of a user-written custom component in a visual business-based orchestration design system according to an embodiment of the present invention;
FIG. 3 is a flow chart of a user's use of custom components in a visual business-based orchestration design system according to an embodiment of the present invention;
fig. 4 is a flow chart of a micro-streaming module loading custom component in a visual business-based orchestration design system according to an embodiment of the present invention.
In the figure:
1. a front canvas; 2. front and back end resolvers; 3. an application starting module; 4. a module is compiled; 5. a component use module; 6. the custom component loading module; 7. and (5) a cloud platform.
Detailed Description
For the purpose of further illustrating the various embodiments, the present invention provides the accompanying drawings, which are a part of the disclosure of the present invention, and which are mainly used to illustrate the embodiments and, together with the description, serve to explain the principles of the embodiments, and with reference to these descriptions, one skilled in the art will recognize other possible implementations and advantages of the present invention, wherein elements are not drawn to scale, and like reference numerals are generally used to designate like elements.
According to the embodiment of the invention, a visual business arrangement design system is provided, which comprises a front canvas and a component, wherein the component mainly comprises a front-end parser, 9 general components, 6 object components, 8 other components and a matched supported runtime parser, the application starting mainly comprises the following links, an application module starts, loads object resources, loads micro-flow resources, traverses micro-flows and initializes micro-flows, and a micro-flow application scene is mainly used in places such as page editing, a field model, a flow engine and the like; taking an http request as an example, the method mainly comprises the following processes and links: http request, request to enter parameter encapsulation, system to enter parameter, business to enter parameter, micro-flow handler processing, a micro-flow, enter parameter encapsulation, general call logic, various business component arrangement and linkage realization, etc.
The method is characterized in that the method mainly comprises the steps of abstracting data processing into various unit components of inquiry, aggregation, submission, circulation and decision, realizing the arrangement design work of each component by means of an editor, then issuing the work to an operating environment, executing relevant configuration logic by means of a scheduler through a configured component result, and comparing the result with a mendix micro-flow component on the market at present, wherein the difference is that the opposite party adopts model driving code to generate, then adopts configuration, then adopts an executor to schedule and execute, and adopts a light application scheduling mode, introduces the idea of fass (Functions as a Service for short), enables the whole system to have high internal cohesion, external low coupling, then adopts a cloud native technology to perform capability development based on the fass mode, and enables a third party to perform development and precipitation of new industrial services and data components based on the current component capability in a very light weight.
The invention will be further described with reference to the accompanying drawings and the specific embodiments, as shown in fig. 1-4, a system for designing service layout based on visualization according to an embodiment of the invention, where the system includes a front canvas 1, a front-end parser 2, an application starting module 3, a component writing module 4, a component using module 5, a custom component loading module 6 and a cloud platform 7;
the front-end canvas 1 is used for providing a canvas at the front end of the system;
the front-end and back-end analyzer 2 is used for analyzing data at the front end and the back end of the system;
the application starting module 3 is used for completing the starting of the application module;
when the application starting module 3 finishes starting the application module, as shown in fig. 1, the application module is started first, and the object resource and the micro-stream resource are loaded, and the micro-stream is traversed to finish initialization.
The whole visual business arrangement system supports user-defined components, the definition components mainly complete the downloading of related development sdk (software development kit) at the components by means of a general component java action component (java is a computer programming language), and the logic arrangement of the related components and the input and output definition of the components are realized.
The component writing module 4 is used for writing templates at the back end and the front end of the custom component;
when writing the back end and front end templates of the custom component in the component writing module 4, if writing the back end templates of the custom component, downloading the back end templates of the custom component, using IDE (integrated development environment) and automatically writing Java codes, packaging the Java codes into jar packages and uploading the jar packages, performing background auditing, after the auditing passes, releasing the component by a user, and dynamically pulling the jar packages by a micro-flow module, and registering component classes with class names designated;
if the front end template of the custom component is written, the front end template of the custom component is downloaded, the front end component code is written, the code is put on shelf and checked in the background, the front end part of the component is released, and the released custom component is automatically loaded when the micro-flow canvas is opened by the front end.
The module using module 5 is used for creating a micro-stream by a user and testing the micro-stream, and after the test is correct, the user issues the micro-stream;
as shown in fig. 3, the module using module 5 creates a micro-stream and tests, and after the test is correct, the user issues the micro-stream further includes the following steps:
a micro-stream is created or edited, and dragging in a custom component and configuring;
clicking a micro-flow test button, observing a test result, and checking an execution log;
after the test result is correct, the user issues the micro-stream.
The custom component loading module 6 is used for starting and running the micro-flow module to finish the registration of component classes in the jar package;
as shown in fig. 4, when the micro-flow module in the custom component loading module 6 is started and operated to complete the registration of the component class in the jar package, if the micro-flow module is started, the jar package information of the published component is pulled from the database, and the jar package is pulled from the minio (high-availability branch deployment object storage service component of open source), so as to complete the registration of the component class in the jar package;
if the micro-flow module operates and the user clicks the component to issue, broadcasting each micro-flow instance, and pulling the jar packet from the minio to finish the registration of the component class in the jar packet;
if the micro-flow module operates and is a timing task, periodically pulling the published jar packet information, and pulling the jar packet from the minio to finish the registration of the component class in the jar packet.
The cloud platform 7 is used for completing one-time configuration multi-place operation of business arrangement.
And when the background is checked, checking the characters and the pictures in the jar packet, and checking the pictures comprises image preprocessing, image feature extraction and image sensitive information retrieval.
When the image is preprocessed, denoising the image, wherein the image to be processed is r (x, y), and the image subjected to mean filtering processing is q (x, y), and the image is expressed as:
wherein omega is a coordinate set of all pixel points in a window neighborhood calculated by the mean filter, K is the total number of all pixel points in the window neighborhood, and x and y are respectively the abscissa and the ordinate in the picture;
the center point of the image is set to (x 0 ,y 0 ) The pixels are arranged in descending order based on gray values, and after all the pixels in the neighborhood taking the center point as the center are ordered, the middle value is taken as the current pixel;
replacing the original gray value of the central pixel with a median value by using a 3X 3 template, filtering the median value to obtain an image f (x, y) subjected to twice filtering, and denoising the image;
for a denoised image f (x, y), the gradient at a certain pixel (x, y) in the image is represented as a vector G:
where f is the de-noised image,is a vector parameter;
calculating the gradient of f (x, y) by using a look-up processing method, and carrying out gradient correction by using the following formula:
wherein T is a non-negative gradient threshold and G [ f (x, y) ] is a gradient vector of the denoised image f (x, y).
When the image features are extracted, the color brightness of the image is represented by a color histogram, and the calculation formula of the color histogram is as follows:
wherein L is a gray value, n l The number of the first gray level pixels is the number of the first gray level pixels, and N is the sum of the image pixels;
and (3) performing image edge detection by using a Sobel operator, wherein the calculation formula of each gray value of an edge detection matrix is as follows:
wherein G is x G (G) y The calculation formulas are respectively used for convolution in the horizontal direction and the vertical direction;
after the edge of the image is detected, the shape characteristic of the image is extracted according to the edge characteristic diagram, the image is rotated from 0 degree to 360 degrees by taking the center of the image as an origin, the number of image pixels with gray values which are not 0 on each angle is calculated, a histogram which takes the angle as an abscissa and takes the number as an ordinate is drawn, the histogram is subjected to normalization processing, the gray distribution characteristic of the image is obtained, and the texture characteristic of the image is extracted;
calculating image texture energy:
wherein p (i, j) is the gray level probability of the image from the horizontal coordinate i to the horizontal coordinate j in a unit distance, and n is a non-zero natural number;
the thickness of the texture is expressed by the moment of inertia, which is expressed as follows:
wherein F is 2 The smaller the image texture is, the shallower the image texture is, n is a non-zero natural number, the complexity of the image texture is represented by an entropy value, and a calculation formula is as follows:
wherein F is 3 The larger the image is, the more complex the image is, n is a non-zero natural number, and the similarity of the row direction and the column direction of textures in the image is obtained according to a calculation formula:
wherein mu is x 、μ y Is the average value of row direction and column direction, sigma x 、σ y In the row direction and column directionVariance, n is a non-zero natural number;
by calculating F 1 、F 2 、F 3 F (F) 4 And (3) obtaining the texture characteristics of the image and extracting the sensitive information in the image.
When the image sensitive information is retrieved, dividing the image into 3 multiplied by 3 blocks, converting the image from RGB space to HSV space, and simultaneously extracting and normalizing the characteristics of the convolutional neural network;
and (5) weighting the image features and fusing the feature vectors, inputting the image features, and completing retrieval of the sensitive information.
And if the image contains text information, screening according to the extracted image characteristics, and detecting text characters.
In summary, the invention adopts a light application scheduling mode, introduces the idea of fass, ensures that the whole system has high internal cohesion and low external coupling, then adopts a cloud native technology to develop the capability based on the fass mode, and ensures that a third party can develop and precipitate new industrial business and data components based on the current component capability in a very light weight way. The whole operation mechanism is very light, high cohesion is adopted in the whole operation mechanism, low coupling is adopted in the whole operation mechanism, more than 20 atomic assemblies are provided for products, more data processing assemblies can be developed based on the atomic assemblies and the opening capability, and the integration and communication of a business system based on the mode with external services and data are realized, so that the business system is supported to realize the cooperation of the business and the data, and the efficiency of developing a core business system is improved through a large number of visual arrangement service precipitates. And the system can automatically audit the characters and pictures of the jar packet, so that the efficiency of developing a core service system is further improved. The invention expresses the arranging logic of the service system in a visual mode, so that the background key logic expression of the service system is not a black box any more; many new industrial components can be developed based on atomic components, including implementing business collaboration and data linkage with other industrial application systems; through the visual proposal, more people can participate in the background logic construction of the business system, which is a supplementary enhancement to the traditional development mode.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. The visual business arrangement-based design system is characterized by comprising a front-end canvas (1), front-end and back-end resolvers (2), an application starting module (3), a component writing module (4), a component using module (5) and a self-defined component loading module (6);
wherein, the front end canvas (1) is used for providing canvas at the front end of the system;
the front-end and back-end analyzer (2) is used for analyzing data at the front end and the back end of the system;
the application starting module (3) is used for finishing the starting of the application module;
the component writing module (4) is used for customizing writing of templates at the rear end and the front end of the component; the component writing module (4) is used for downloading the self-defined component back-end template, using IDE and automatically writing Java codes when the self-defined component back-end template is written, packaging the Java codes into a jar package and uploading the jar package, and after the jar package passes the background examination, issuing the component by a user, dynamically pulling the jar package by the micro-flow module, and registering the component class with the class name designated; during background auditing, auditing characters and pictures in the jar packet, wherein auditing the pictures comprises image preprocessing, image feature extraction and image sensitive information retrieval; when the image is preprocessed, denoising the image, wherein the image to be processed is r (x, y), and the image subjected to mean filtering processing is q (x, y), and the image is expressed as:
wherein omega is a coordinate set of all pixel points in a window neighborhood calculated by the mean filter, K is the total number of all pixel points in the window neighborhood, and x and y are respectively the abscissa and the ordinate in the picture;
the center point of the image is set to (x 0 ,y 0 ) The pixels are arranged in descending order based on gray values, and after all the pixels in the neighborhood with the center point as the center are ordered, the middle value is taken as the current pixel;
replacing the original gray value of the central pixel with a median value by using a 3X 3 template, filtering the median value to obtain an image f (x, y) subjected to twice filtering, and denoising the image;
for a denoised image f (x, y), the gradient at a certain pixel (x, y) in the image is represented as a vector G:
where f is the de-noised image,is a vector parameter;
calculating the gradient of f (x, y) by using a differential processing method, and performing gradient correction by using the following formula:
wherein T is a non-negative gradient threshold, G is a gradient vector of the denoising image f (x, y);
when the self-defining component back end and front end template in the component writing module (4) is written, if the self-defining component front end template is written, the self-defining component front end template is downloaded, a front end component code is written, the code is put on shelf and checked in the background, the front end part of the component is issued, and when the micro-flow canvas is opened, the front end automatically loads the issued self-defining component;
the component using module (5) is used for creating a micro-stream by a user and testing the micro-stream, and after the test is correct, the user issues the micro-stream;
the custom component loading module (6) is used for starting and running the micro-flow module to finish the registration of component classes in the jar package;
when the image features are extracted, the color brightness of the image is represented by a color histogram, and the calculation formula of the color histogram is as follows:
wherein L is a gray value, n l The number of the first gray level pixels is the number of the first gray level pixels, and N is the sum of the image pixels;
and (3) performing image edge detection by using a Sobel operator, wherein the calculation formula of each gray value of an edge detection matrix is as follows:
wherein G is x G (G) y The calculation formulas are respectively used for convolution in the horizontal direction and the vertical direction;
calculating image texture energy:
wherein F is 1 For calculating the function of the image texture energy, p (i, j) is the gray level probability of the image from the horizontal coordinate i to the horizontal coordinate j in a unit distance, and n is a non-zero natural number;
the thickness of the texture is expressed by the moment of inertia, which is expressed as follows:
wherein F is 2 F is a moment of inertia function representing the thickness of the image texture 2 The smaller the image texture isShallow, n is a non-zero natural number, the complexity of the image texture is represented by an entropy value, and a calculation formula is as follows:
wherein F is 3 To represent entropy function of complexity of image texture, F 3 The larger the image is, the more complex the image is, n is a non-zero natural number, and the similarity of the row direction and the column direction of textures in the image is obtained according to a calculation formula:
wherein F is 4 Mu, a similarity function representing the row and column directions of the image texture x 、μ y Is the average value of row direction and column direction, sigma x 、σ y The variance is in the row direction and the column direction, and n is a non-zero natural number;
by calculating F 1 、F 2 、F 3 F (F) 4 The mean and variance of the image are obtained, and sensitive information in the image is extracted;
when the image sensitive information is retrieved, dividing the image into 3 multiplied by 3 blocks, converting the image from RGB space to HSV space, and simultaneously extracting and normalizing the characteristics of the convolutional neural network;
and (5) weighting the image features and fusing the feature vectors, inputting the image features, and completing retrieval of the sensitive information.
2. The visual business arrangement design system according to claim 1, wherein when the micro-flow module in the custom component loading module (6) is started and operated to complete the registration of the component class in the jar package, if the micro-flow module is started, the jar package information of the published component is pulled from the database, and the jar package is pulled from the minio to complete the registration of the component class in the jar package.
3. The visual business arrangement design system according to claim 2, wherein in the custom component loading module (6), if the micro-flow module operates and the user clicks the component release, each micro-flow instance is broadcasted, and the jar packet is pulled from the mini to complete the registration of the component class in the jar packet;
if the micro-flow module operates and is a timing task, periodically pulling the published jar packet information, and pulling the jar packet from the minio to finish the registration of the component class in the jar packet.
4. The visual business arrangement design system of claim 1, wherein the image includes text information, and wherein the image is filtered based on the extracted image features and text characters are detected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210914882.7A CN115328463B (en) | 2022-08-01 | 2022-08-01 | Visual business arrangement design system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210914882.7A CN115328463B (en) | 2022-08-01 | 2022-08-01 | Visual business arrangement design system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115328463A CN115328463A (en) | 2022-11-11 |
| CN115328463B true CN115328463B (en) | 2023-07-21 |
Family
ID=83919791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210914882.7A Active CN115328463B (en) | 2022-08-01 | 2022-08-01 | Visual business arrangement design system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115328463B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117194588B (en) * | 2023-11-07 | 2024-01-19 | 江苏龙虎网信息科技股份有限公司 | Business data integrated supervision system and method based on big data |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022037690A1 (en) * | 2020-08-20 | 2022-02-24 | 第四范式（北京）技术有限公司 | Application construction system, method and platform, and application publication system, method and platform |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9395959B2 (en) * | 2011-12-09 | 2016-07-19 | Microsoft Technology Licensing, Llc | Integrated workflow visualization and editing |
| US10409575B2 (en) * | 2018-02-09 | 2019-09-10 | Cognizant Technology Solutions India Pvt. Ltd. | System and method for developing software applications of wearable devices |
| CN109284756A (en) * | 2018-08-01 | 2019-01-29 | 河海大学 | A kind of terminal censorship method based on OCR technique |
| CN111258567B (en) * | 2018-12-01 | 2023-06-16 | 阿里巴巴集团控股有限公司 | Service code development processing method and device |
| CN110059468B (en) * | 2019-04-02 | 2023-09-26 | 创新先进技术有限公司 | Applet risk identification method and device |
| CN112287270A (en) * | 2020-09-28 | 2021-01-29 | 珠海大横琴科技发展有限公司 | Content auditing method and device |
| CN114546670A (en) * | 2020-11-25 | 2022-05-27 | 浙江大学 | Coroutine-based functional asynchronous data distribution system and method |
| CN112691380B (en) * | 2020-12-31 | 2021-12-17 | 完美世界(北京)软件科技发展有限公司 | Game resource material auditing method and device, storage medium and computer equipment |
| CN113849156B (en) * | 2021-09-17 | 2022-06-28 | 中国科学院软件研究所 | Codeless intelligent development system applied to OpenHarmony operating system and application method thereof |
-
2022
- 2022-08-01 CN CN202210914882.7A patent/CN115328463B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022037690A1 (en) * | 2020-08-20 | 2022-02-24 | 第四范式（北京）技术有限公司 | Application construction system, method and platform, and application publication system, method and platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115328463A (en) | 2022-11-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3692438B1 (en) | Automatic generation of a graphic user interface (gui) based on a gui screen image | |
| CN109543690B (en) | Method and device for extracting information | |
| CN111914835A (en) | Bill element extraction method and device, electronic equipment and readable storage medium | |
| CN115328463B (en) | Visual business arrangement design system | |
| CN110334214A (en) | A kind of method of false lawsuit in automatic identification case | |
| CN112052840B (en) | Picture screening method, system, equipment and storage medium | |
| CN110135421A (en) | Licence plate recognition method, device, computer equipment and computer readable storage medium | |
| Oza et al. | Insurance claim processing using RPA along with chatbot | |
| CN113221947A (en) | Industrial quality inspection method and system based on image recognition technology | |
| CN110162657A (en) | A kind of image search method and system based on high-level semantics features and color characteristic | |
| CN112949455A (en) | Value-added tax invoice identification system and method | |
| CN114973290A (en) | Intelligent order examination method and system based on OCR engine | |
| Abd-Elrahman et al. | Deep learning classification of high-resolution drone images using the arcgis pro software: For374/fr444, 10/2021 | |
| CN111414889B (en) | Financial statement identification method and device based on character identification | |
| CN113505780A (en) | Two-dimensional code-based intelligent detection maintenance method and equipment | |
| Saito et al. | Improving active learning with sharp data reduction | |
| CN114708582A (en) | AI and RPA-based intelligent electric power data inspection method and device | |
| CN114625633A (en) | Method, system and storage medium for interface testing | |
| Lee | Robotics Process Automation (RPA) And The Import/Export Customs Declaration Process | |
| CN114359945A (en) | Method and device for generating express delivery abnormal bill, computer equipment and storage medium | |
| CN112363847B (en) | Automatic identification method and system for license document | |
| Minouei et al. | Efficient table border segmentation with asymmetric convolutions | |
| Ahmed et al. | KERAS DEEP LEARNING PACKAGE IN PYTHON: A REVIEW | |
| CN110379250B (en) | Calligraphy grid bounding method and equipment | |
| Fadhel et al. | Employing Parallel Hardware Architectures to Diagnose Sickle Cell Anemia in Real-Time Basis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |